LOCAL FEATURE BASED IMAGE SIMILARITY FUNCTIONS FOR KNN CLASSIFICATION

Giuseppe Amato, Fabrizio Falchi

Abstract

In this paper we consider the problem of image content recognition and we address it by using local features and kNN based classification strategies. Specifically, we define a number of image similarity functions relying on local feature similarity and matching with and without geometric constrains. We compare their performance when used with a kNN classifier. Finally we compare everything with a new kNN based classification strategy that makes direct use of similarity between local features rather than similarity between entire images. As expected, the use of geometric information offers an improvement over the use of pure image similarity. However, surprisingly, the kNN classifier that use local feature similarity has a better performance than the others, even without the use of geometric information. We perform our experiments solving the task of recognizing landmarks in photos.

References

  1. Amato, G., Falchi, F., and Bolettieri, P. (2010). Recognizing landmarks using automated classification techniques: an evaluation of various visual features. In in Proceeding of The Second Interantional Conference on Advances in Multimedia (MMEDIA 2010), Athens, Greece, 13-19 June 2010, pages 78-83. IEEE Computer Society.
  2. Ballard, D. H. (1981). Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition, 13(2):111-122.
  3. Batko, M., Novak, D., Falchi, F., and Zezula, P. (2008). Scalability comparison of peer-to-peer similarity search structures. Future Generation Comp. Syst., 24(8):834-848.
  4. Bay, H., Tuytelaars, T., and Gool, L. J. V. (2006). Surf: Speeded up robust features. In ECCV (1), pages 404- 417.
  5. Boiman, O., Shechtman, E., and Irani, M. (2008). In defense of nearest-neighbor based image classification. In CVPR.
  6. Chen, T., Wu, K., Yap, K.-H., Li, Z., and Tsai, F. S. (2009). A survey on mobile landmark recognition for information retrieval. In MDM 7809: Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, pages 625-630, Washington, DC, USA. IEEE Computer Society.
  7. Dudani, S. (1975). The distance-weighted k-nearestneighbour rule. IEEE Transactions on Systems, Man and Cybernetics, SMC-6(4):325-327.
  8. Fagni, T., Falchi, F., and Sebastiani, F. (2010). Image classification via adaptive ensembles of descriptor-specific classifiers. Pattern Recognition and Image Analysis, 20:21-28.
  9. Falchi, F. (2010). Pisa landmarks http://www.fabriziofalchi.it/pisaDataset/. cessed on 30-March-2010.
  10. Google (2010). Google Goggles. http://www.google.com/ mobile/goggles/. last accessed on 30-March-2010.
  11. J├ęgou, H., Douze, M., and Schmid, C. (2010). Improving bag-of-features for large scale image search. Int. J. Comput. Vision, 87(3):316-336.
  12. Kennedy, L. S. and Naaman, M. (2008). Generating diverse and representative image search results for landmarks. In WWW 7808: Proceeding of the 17th international conference on World Wide Web, pages 297-306, New York, NY, USA. ACM.
  13. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
  14. Samet, H. (2005). Foundations of Multidimensional and Metric Data Structures. Computer Graphics and Geometric Modeling. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
  15. Serdyukov, P., Murdock, V., and van Zwol, R. (2009). Placing flickr photos on a map. In Allan, J., Aslam, J. A., Sanderson, M., Zhai, C., and Zobel, J., editors, SIGIR, pages 484-491. ACM.
  16. Yeh, T., Tollmar, K., and Darrell, T. (2004). Searching the web with mobile images for location recognition. In CVPR (2), pages 76-81.
  17. Zezula, P., Amato, G., Dohnal, V., and Batko, M. (2006). Similarity Search: The Metric Space Approach, volume 32 of Advances in Database Systems. SpringerVerlag.
  18. Zheng, Y., 0003, M. Z., Song, Y., Adam, H., Buddemeier, U., Bissacco, A., Brucher, F., Chua, T.-S., and Neven, H. (2009). Tour the world: Building a web-scale landmark recognition engine. In CVPR, pages 1085-1092. IEEE.
Download


Paper Citation


in Harvard Style

Amato G. and Falchi F. (2011). LOCAL FEATURE BASED IMAGE SIMILARITY FUNCTIONS FOR KNN CLASSIFICATION . In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-40-9, pages 157-166. DOI: 10.5220/0003185401570166


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - LOCAL FEATURE BASED IMAGE SIMILARITY FUNCTIONS FOR KNN CLASSIFICATION
SN - 978-989-8425-40-9
AU - Amato G.
AU - Falchi F.
PY - 2011
SP - 157
EP - 166
DO - 10.5220/0003185401570166


in Bibtex Style

@conference{icaart11,
author={Giuseppe Amato and Fabrizio Falchi},
title={LOCAL FEATURE BASED IMAGE SIMILARITY FUNCTIONS FOR KNN CLASSIFICATION},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2011},
pages={157-166},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003185401570166},
isbn={978-989-8425-40-9},
}